{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generative Adversarial Nets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training a generative adversarial network to sample from a Gaussian distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import norm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geneate target distribution $p_{data}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fce0ef8d30>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fShpEkvQNjdM7OA68crOT85PcjhqiEMlsShpEkrxqUSclDQuiqryQqvJCQMWQ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\nS/jT39tOKATDY1G+8J199A6OBx2WSCC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fcdb005668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = -1.0\n",
    "sigma = 1.0\n",
    "xs = np.linspace(-5, 5, 1000)\n",
    "plt.plot(xs, norm.pdf(xs, loc=mu, scale=sigma))\n",
    "# Plot the target distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Preparation (MLP Model and Optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_epochs = 10000\n",
    "batch_size = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# MLP Model\n",
    "def mlp(input_tensor, out_dim):\n",
    "    \"\"\"\n",
    "    Construct a mlp model \n",
    "    :param input_tensor: 2-D tensor\n",
    "    :param out_dim: int, the dimension of the output\n",
    "    \"\"\"\n",
    "    in_dim = input_tensor.get_shape().as_list()[-1]\n",
    "    W0 = tf.get_variable(\"W0\", [in_dim, 6], initializer=tf.random_normal_initializer())\n",
    "    b0 = tf.get_variable(\"b0\", [6, ], initializer=tf.constant_initializer(0.0))\n",
    "    W1 = tf.get_variable(\"W1\", [6, 5], initializer=tf.random_normal_initializer())\n",
    "    b1 = tf.get_variable(\"b1\", [5, ], initializer=tf.constant_initializer(0.0))\n",
    "    W2 = tf.get_variable(\"W2\", [5, out_dim], initializer=tf.random_normal_initializer())\n",
    "    b2 = tf.get_variable(\"b2\", [out_dim, ], initializer=tf.constant_initializer(0.0))\n",
    "    # Use tanh activation function \n",
    "    fc0 = tf.nn.tanh(tf.nn.xw_plus_b(input_tensor, W0, b0))\n",
    "    fc1 = tf.nn.tanh(tf.nn.xw_plus_b(fc0, W1, b1))\n",
    "    output = tf.nn.tanh(tf.nn.xw_plus_b(fc1, W2, b2))\n",
    "    return output, [W0, b0, W1, b1, W2, b2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use monmentum optimizer for training\n",
    "def momentum_optimizer(loss, var_list):\n",
    "    \"\"\"\n",
    "    Return the train operation by defining monmentum optimizer\n",
    "    \"\"\"\n",
    "    global_step = tf.Variable(0, trainable=False)\n",
    "    lr = tf.train.exponential_decay(0.001, global_step, decay_steps=train_epochs//4, \n",
    "                                    decay_rate=0.95, staircase=True)\n",
    "    train_op = tf.train.MomentumOptimizer(lr, 0.6).minimize(loss, global_step=global_step,\n",
    "                                                            var_list=var_list)\n",
    "    return train_op"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-train Discriminative Model ($D$)\n",
    "We firstly pre-train the discriminative model for faster convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.variable_scope(\"D_pre\"):\n",
    "    input = tf.placeholder(tf.float32, shape=[batch_size, 1])\n",
    "    target = tf.placeholder(tf.float32, shape=[batch_size, 1])\n",
    "    D, param_D = mlp(input, 1)\n",
    "    loss = tf.reduce_mean(tf.square(D - target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_op = momentum_optimizer(loss, param_D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Ploter for decision surface\n",
    "def ploter_d0(D, input):\n",
    "    f, ax = plt.subplots(1)\n",
    "    # Get p_data\n",
    "    xs = np.linspace(-5, 5, 1000)\n",
    "    ax.plot(xs, norm.pdf(xs, loc=mu, scale=sigma), label=\"p_data\")\n",
    "    # Plot the decision surface\n",
    "    r = 1000 # number of points\n",
    "    xs = np.linspace(-5, 5, r)\n",
    "    ds = np.zeros(r)\n",
    "    # Get the decision surface \n",
    "    for i in range(r//batch_size):\n",
    "        x = np.reshape(xs[batch_size*i:batch_size*(i+1)], [batch_size, 1])\n",
    "        ds[batch_size*i:batch_size*(i+1)] = np.reshape(sess.run(D, feed_dict={input: x}), [-1])\n",
    "    ax.plot(xs, ds, label=\"decision boundary\")\n",
    "    ax.set_ylim(0, 1.2)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1fce123c198>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r4IgG2m4HhhhjjjLGuIBJQCGwKpxARUREpGWFWhR0BfKstdW1jm0B4owxHeu1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\nUiCEEEKImP8HFPNejiwW/oQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fce122bc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ploter_d0(D, input)\n",
    "plt.title(\"Initial Decision Boundary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pre-train D\n",
    "losses = np.zeros(1000)\n",
    "for i in range(1000):\n",
    "    x = (np.random.random(batch_size) - 0.5)*10.0  # to ensure the data equally distributed\n",
    "    y = norm.pdf(x, loc=mu, scale=sigma)\n",
    "    l, _ = sess.run([loss, train_op], feed_dict={input:np.reshape(x, [batch_size,1]),\n",
    "                                                target:np.reshape(y, [batch_size, 1])})\n",
    "    losses[i] = l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1fce12c89e8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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be1ugUCjYaa2MB557j/Nmj+/t5eJBKBTs9Ldknurcf6pz/6nO/edXXXsNGa10DROJ7W4H\nbFpr9wJ7gZXGmPcBXwZeBy4E8hMtG+5MlG3AR4GHelugiopiCvJDnfZVVpb2cLb0h4qK4mwXYchR\nnftPde4/1Xnu8RoydgDVxpigtTYxAKIGCFtrG1JPNMacBkSttW+n7H4Hp5sEa20HkHxsqrW2zRiz\nCRjnpUCNjWGcma+H1Nf3uiFEPAiFglRUFNPYGNYgW5+ozv2nOvef6tx/iTrPNK8hYzlOMJgDLHb3\nzQPe6Obca4DJwIdS9s3GmVmCMWY9zuyT+e52KXAc8J6XAkWjsU4PSQOIRPSPNJOi0Zjq2Geqc/+p\nzv2nOs89nkKGtTZsjJkP3GuMuRoYjzMt9SoAY8xo4IC1thW4D1hqjPka8BxwJXA68Fn3ds8APzTG\nbAHqgFuBrcCzXj9E6pgMERERGRj68u18PbAMeAm4C7jJWvuke2wXcDmA201yKfAFYAVOi8YHrbW7\n3XO/DTwC/B5Y6pblImut5xW1gmktGSIiIpJ9npcVt9aGgc+7f9KPBdO2n6WHlglrbTtO0Pi21zKk\nS31ImoiIiAwMOdHPkKeWDBERkQEnJ0JG+sBPERERyb7cCBlawEVERGTAyYlv52BALRkiIiIDTU6E\njPSMEdMj30VERLIuR0JG2mJcWjFOREQk63IiZBSmPbtEIUNERCT7ciJkFBV2DhmLVu7KUklEREQk\nITdCRkHnkPHHl9ZnqSQiIiKSkCMhw/PCpSIiIpJhOREyitNaMkRERCT7ciJkTBhV1ml72sTKLJVE\nREREEnIiZJhjKvnEB47NdjFEREQkRU6EDICL3jeJ2cePBCCqKawiIiJZlzMhAw498j2qFT9FRESy\nLrdCRtD5OJGoQoaIiEi25VbISLZkqLtEREQk23IqZOSF1JIhIiIyUORUyAgF1ZIhIiIyUORUyMhz\nu0vUkiEiIpJ9ORUyEgM/NbtEREQk+3IqZCRaMrROhoiISPZ5frKYMaYQuBu4DGgB7rDW/qyHc68A\n/h2YALwFfMta+0bK8U8DtwJjgL8A/2Kt3ee1TAmJMRkRtWSIiIhkXV9aMm4HZgHnANcCNxtjLks/\nyRhzNnA/8B/AdGAJ8JwxpsQ9foZ7/GbgTKASeKAP5UlKzC5RS4aIiEj2eQoZbkC4Bvi6tXaFtfZJ\n4Dbgum5OrwFusdb+wVq7GbgFGIETOAC+CvzRWvt7a+1q4ErgI8aYiX37KCmzS6Jx4nG1ZoiIiGST\n15aMmThdLEtS9r2C0xLRibX2EWvtjwGMMUXA9UAt8I57yhxgYcr524Gt7v4+CbktGXFg5YY+97qI\niIhIP/AaMsYAddbaSMq+WqDIGFPV3QXGmPOAJuAm4JvW2paUe+1MO70WGO+xTEmJFT8Bfv7ISjoi\n0b7eSkRERI6S15BRArSl7UtsF/ZwzSqcMRz/DjzojsU43L16us8R5QU7f5xwm0KGiIhItnidXdJK\n1xCQ2G6hG9bavcBeYKUx5n3Al4HXD3Ovbu/Tk0QXCUBBfueQ0RGNkZeXU7N0sypR16l1LpmlOvef\n6tx/qnP/+VXXXkPGDqDaGBO01iamcNQAYWttQ+qJxpjTgKi19u2U3e8A01LuVZN2/xpgl5cCVVQU\nJ18PG1bc6VhBUQGVlaVebie9kFrn4g/Vuf9U5/5TneceryFjOdCBMzhzsbtvHvBGN+deA0wGPpSy\nbzbwpvt6KXA2MB/AGDMBZzzGUi8FamwMJ6esFgQ6H6vde5DKEs9LgUgPQqEgFRXFnepcMkt17j/V\nuf9U5/5L1HmmefoGttaGjTHzgXuNMVfjhIIbgKsAjDGjgQPW2lbgPmCpMeZrwHM4U1RPd/8GuAdY\nYIxZihM87gSestZu8VKmaDRGJOL8o6waVtTpWHO4I3lM+k9qnYs/VOf+U537T3Wee/rSKXM9sAx4\nCbgLuMldLwOcro7LAdxukkuBLwArcFo0Pmit3eUeXwp8CWcxrleAfcDVff4kQHlxfqftcHukhzNF\nREQk0wKDfNGqeH19c6fke++Tq3n93T0AfO5DhnNOGZetsuWcvLwglZWlpNe5ZI7q3H+qc/+pzv3n\n1nngyGcenZwbyvulS05Mvm7VFFYREZGsybmQEQgEKC1yhpq0qrtEREQka3IuZADku2tjdKjZTURE\nJGsUMkRERCQjcjRkhABoV8gQERHJmhwNGWrJEBERybYcDxmaXSIiIpItuRkyQmrJEBERybacDBkF\niZYMrYEvIiKSNTkZMhLdJRr4KSIikj05GjKc2SXqLhEREcmeHA0ZGpMhIiKSbTkeMjS7REREJFty\nMmQUqCVDREQk63IyZKi7REREJPtyOmS0dUSJx+NZLo2IiMjQlJMho6K0AIBINE5zqx73LiIikg05\nGTJGVBQlX+9vbM1iSURERIau3AwZ5YXJ1/sPtmWxJCIiIkNXboaMlJaMerVkiIiIZEVOhozC/BCl\nRXmAWjJERESyJSdDBhxqzdCYDBERkezI3ZDhjsvY36iWDBERkWzI83qBMaYQuBu4DGgB7rDW/qyH\ncy8CfgRMBTYAN1lrn0o53gCUAwF3Vxwot9a2eC1XumRLxkG1ZIiIiGRDX1oybgdmAecA1wI3G2Mu\nSz/JGHMy8ChwPzATuA94xBgzwz0+FidgHAvUuH/G9EfAACgrzgegRetkiIiIZIWnlgxjTAlwDXCh\ntXYFsMIYcxtwHfBY2umfBl601v7S3b7bGHMJcDmwCpgG7LLWbjmaD9CTwgLnce9tHXpImoiISDZ4\n7S6Z6V6zJGXfK8CN3Zz7AFDQzf7EIhbTgbUe37/XCvOdkBGJxolEY+SFcnb4iYiIyIDkNWSMAeqs\ntal9ELVAkTGmylq7L7HTWmtTLzTGnAicD/y3u2saUGqMWQAY4G3gm9badR7L1K0ityUDoL0jqpAh\nIiLiM68howRIn66R2C6kB8aYapzxGYustU+7u08AKoHvAQfdv180xkyz1jb3tkChHsJDSdGhjxaJ\nxcnLU8g4Wom67qnOpf+pzv2nOvef6tx/ftW115DRStcwkdjudsCmMWY08ALOzJFPpRy6EMhPDPQ0\nxlwBbAM+CjzU2wJVVBR3u79qRGnydUFRAZWVpd2eJ971VOeSOapz/6nO/ac6zz1eQ8YOoNoYE7TW\nxtx9NUDYWtuQfrIxZhzwEhAFzknrTukAOlK224wxm4BxXgrU2BgmGo112d/RdqhHZ09dE2UFSshH\nKxQKUlFR3GOdS/9TnftPde4/1bn/EnWeaV5DxnKcYDAHWOzumwe8kX6iOxPleff8c621e9OOrwdu\nsdbOd7dLgeOA97wUKBqNEYl0/UeZHwokX4dbO7o9R/qmpzqXzFGd+0917j/Vee7xFDKstWFjzHzg\nXmPM1cB44AbgKkh2jRyw1rYC3wcm46ynEXSPgdPq0Qg8A/zQGLMFqANuBbYCzx71p+LQFFaA1nZN\nYxUREfFbX/oQrgeW4XSD3IWziueT7rFdOOtggLMiaDHwGrAz5c+d7vHvAI8AvweWumW5yFob70OZ\nuijKV8gQERHJJs/Liltrw8Dn3T/px4Ipr6cd4T5twLfdP/0uPy9lCmtEIUNERMRvOTsaMj/v0JiM\nSLRfGkdERETEg5wNGalzgDWQSERExH85GzKCgQChoNOaEdGUKBEREd/lbMgAkqt8dihkiIiI+C6n\nQ0a+22WilgwRERH/5XTIyHMX5IpENPBTRETEbzkeMtRdIiIiki05HTLy89RdIiIiki05HTISLRma\nwioiIuK/nA4Z+ZpdIiIikjU5HTKSLRla8VNERMR3OR0yEo9715gMERER/+V0yEjOLtGYDBEREd/l\ndsjQ7BIREZGsyemQoRU/RUREsienQ4a6S0RERLInp0NGYX4IgLaOaJZLIiIiMvTkdMgoKnRCRmu7\nQoaIiIjfcjtkFChkiIiIZEuOh4w8wBmTocGfIiIi/srxkBFKvlZrhoiIiL9yPGTkJV+3tkeyWBIR\nEZGhJ+/Ip3RmjCkE7gYuA1qAO6y1P+vh3IuAHwFTgQ3ATdbap1KOfxq4FRgD/AX4F2vtPq9l6kli\n4CeoJUNERMRvfWnJuB2YBZwDXAvcbIy5LP0kY8zJwKPA/cBM4D7gEWPMDPf4Ge6xm4EzgUrggT6U\np0fFqS0ZbQoZIiIifvLUkmGMKQGuAS601q4AVhhjbgOuAx5LO/3TwIvW2l+623cbYy4BLgdWAV8F\n/mit/b177yuBLcaYidbaLX3+RClSx2SE1V0iIiLiK68tGTNxgsmSlH2v4LREpHsA+F43+wvdv+cA\nCxM7rbXbga3u/n5RXpKffN3Y3N5ftxUREZFe8BoyxgB11trUZoFaoMgYU5V6onWsSmwbY04EzudQ\nsBgD7Ey7fy0w3mOZelRWnJ9cWryhqa2/bisiIiK94HXgZwmQ/m2d2C6kB8aYapzxGYustU8f4V49\n3qc7odDhc1JleSF7G8Ls2teSfCqr9E2iro9U59J/VOf+U537T3XuP7/q2mvIaKVrCEhst3R3gTFm\nNPACEAc+1Yt7dXufnlRUFB/2ePXwYvY2hFm8ejcnTq3mknlTvNxeunGkOpf+pzr3n+rcf6rz3OM1\nZOwAqo0xQWttYgnNGiBsrW1IP9kYMw54CYgC56RNT93hXpuqBtjlpUCNjWGih1nNs6zo0Ef89ROr\nmXdS+ltKb4VCQSoqio9Y59J/VOf+U537T3Xuv0SdZ5rXkLEc6MAZnLnY3TcPeCP9RHcmyvPu+eda\na/emnbIUOBuY754/AWc8xlIvBYpGY0QO8yj3/LQuksOdK71zpDqX/qc695/q3H+q89zjKWRYa8PG\nmPnAvcaYq3FCwQ3AVZDsGjlgrW0Fvg9MxllPI+geA6fVoxG4B1hgjFkKvAncCTzVX9NXExKPexcR\nERF/9WXkx/XAMpxukLtwVvF80j22C2cdDHBWBC0GXsOZRZL4cyeAtXYp8CWcxbheAfYBV/fpUxyG\nQoaIiEh2eF5W3FobBj7v/kk/Fkx5Pa0X95qP212SKQX5Gq0sIiKSDTn/DVxYoJYMERGRbMj5kJGn\nedciIiJZkfPfwIFsF0BERGSIyvmQkS4Wj2e7CCIiIkNCzoeMQKBzW0aH5mCLiIj4IudDRrr2jmi2\niyAiIjIk5HzImDpuWKdttWSIiIj4I+dDxsSacuadPCa53aaWDBEREV/kfMgAmJvyULT2DrVkiIiI\n+GFIhIyClKXF1V0iIiLij6ERMlKexNoWUXeJiIiIH4ZEyMhPbclQd4mIiIgvhkTIKExpyWhXS4aI\niIgvhkTIyM871JKhgZ8iIiL+GBIhI/Vx72rJEBER8ceQCBl5oSBBd3lxtWSIiIj4Y0iEDDjUmqGW\nDBEREX8MnZDhDv7UOhkiIiL+GDohw53GqmXFRURE/DHkQoaewioiIuKPIRMyyovzAWhoas9ySURE\nRIaGIRMyRo8oBqB2fwuxeDzLpREREcl9QydkVJYAUFsf5mt3LmTL7oNZLpGIiEhuy/N6gTGmELgb\nuAxoAe6w1v7sCNecDTxorZ2Str8BKAcC7q44UG6tbfFariMZU12afB1ui3L/0+9w6xfO7O+3ERER\nEZfnkAHcDswCzgEmAfONMZuttY91d7IxZgbwMBBO2z8WJ2Acm3osEwED4IRjhnfabmrtyMTbiIiI\niMtTyDDGlADXABdaa1cAK4wxtwHXAV1ChjHmS8BPgQ3AsLTD04Bd1totfSm4V0UFeZQU5tHSFgGg\nMOXJrCIiItL/vI7JmIkTTJak7HsF6Knf4Xzgs8Cd3RybDqz1+P5HJRgMJF8rZIiIiGSW15AxBqiz\n1kZS9tUCRcaYqvSTrbWXW2v/3MO9pgGlxpgFxpidxphnjDHHeSyPJ6GQQoaIiIhfvI7JKAHa0vYl\ntgs93usEoBL4HnDQ/ftFY8w0a21zb28SCvU+J4VSWjKKCkLk5Q2ZyTX9IlHXXupcjo7q3H+qc/+p\nzv3nV117DRmtdA0TiW2vAzYvBPITAz2NMVcA24CPAg/19iYVFcW9fsP8vEOtF2WlBVRWlh7mbOmJ\nlzqX/qE695/q3H+q89zjNWTsAKqNMUFrbeJJYzVA2Frb4OVG1toOoCNlu80YswkY5+U+jY1hotHe\nPfRsytgeVwImAAAgAElEQVQKavc7WSgI1Nf3usFEcJJvRUWxpzqXo6M695/q3H+qc/8l6jzTvIaM\n5TjBYA6w2N03D3jD6xsbY9YDt1hr57vbpcBxwHte7hONxoj08smql583lcWrdwMQj8d7fZ105qXO\npX+ozv2nOvef6jz3eAoZ1tqwMWY+cK8x5mpgPHADcBWAMWY0cMBa29qL2z0D/NAYswWoA24FtgLP\neimTFxUlBUwZW8GGnY1EY1paXEREJJP6MvLjemAZ8BJwF3CTtfZJ99gu4PJe3ufbwCPA74Glblku\nstZm9Ns/MfgzGlXIEBERySTPK35aa8PA590/6ce6DS3W2geBB9P2teMEjW97LcPRSIyoVUuGiIhI\nZg25+UKJloxITP1+IiIimTRkQ4a6S0RERDJr6IUMdZeIiIj4YuiFjERLhrpLREREMmrohYyQuktE\nRET8MPRCRiDRkqGQISIikklDL2SEFDJERET8MPRCRtAd+Kn18UVERDJqCIYMtWSIiIj4YeiFDLe7\npO5AKxG1ZoiIiGTMkAsZ8ZQGjF/9eU32CiIiIpLjhlzI2LizMfl6md2bxZKIiIjktiEXMs6aUZN8\nXVacn8WSiIiI5LYhFzLmnjSGERWF2S6GiIhIzhtyISM/L8j7Tx4LaGlxERGRTBpyIQMOzTAJt0W5\n9cE3WbmhLsslEhERyT1DM2QED33sTbsaufPhlVksjYiISG4aoiEjkO0iiIiI5LyhGTJCChkiIiKZ\nNjRDhloyREREMm6Ihowh+bFFRER8NSS/bfPUXSIiIpJxeV4vMMYUAncDlwEtwB3W2p8d4ZqzgQet\ntVPS9n8auBUYA/wF+Bdr7T6vZfIqFBqS2UpERMRXffm2vR2YBZwDXAvcbIy5rKeTjTEzgIeBQNr+\nM4D7gZuBM4FK4IE+lMczjckQERHJPE8hwxhTAlwDfN1au8Ja+yRwG3BdD+d/CXgV2N3N4a8Cf7TW\n/t5auxq4EviIMWailzL1hUKGiIhI5nltyZiJ08WyJGXfKzgtEd05H/gscGc3x+YACxMb1trtwFZ3\nf0ZpCquIiEjmeQ0ZY4A6a20kZV8tUGSMqUo/2Vp7ubX2z4e51860fbXAeI9l8qy72SWxeDzTbysi\nIjKkeB34WQK0pe1LbHt9tGlP9/J0n74M4izID3W7Py9PA0IPJ1HXGjjrH9W5/1Tn/lOd+8+vuvYa\nMlrpGgIS2y39dC9P96moKPb4tlDZmJ5toLS8mLLifM/3Gor6UudydFTn/lOd+091nnu8howdQLUx\nJmitTTwnvQYIW2sb+nCvmrR9NcAuLzdpbAwTjXp7ZHtLc9eQsW9fEx2lBZ7uM9SEQkEqKor7VOfS\nN6pz/6nO/ac691+izjPNa8hYDnTgDM5c7O6bB7zRh/deCpwNzAcwxkzAGY+x1MtNotEYkYjHf5Td\nDL9obYtQUuh52ZAhqU91LkdFde4/1bn/VOe5x9O3qrU2bIyZD9xrjLkaJxTcAFwFYIwZDRyw1rb2\n4nb3AAuMMUuBN3FmoDxlrd3ipUx90d3skg6lZxERkX7Vl5Ef1wPLgJeAu4Cb3PUywOnquLw3N7HW\nLgW+hLMY1yvAPuDqPpTHs+7Wydiy+6Afby0iIjJkBOKDe+pmvL6+2XPz2o66Zm66/7Uu+3/6lblU\nDSvqr7LlnLy8IJWVpfSlzqVvVOf+U537T3XuP7fOM75o1JCcLzS8rPsBngtXpC/bISIiIn01JENG\naVE+3/3MqVxz0TTKSw5NW91Sqy4TERGR/jIkQwaAOaaSs2aM4eufPDm5b+WGfTSFO7JYKhERkdwx\nZENGwpSxw7jyQpPcfu61jE9uERERGRKGfMgAmDN9dPL1hu0HslgSERGR3KGQARQX5nHxXOcJ85tr\nDzLIZ9yIiIgMCAoZrlHDSwBo74jR0hY5wtkiIiJyJAoZrmEp01oPNLVnsSQiIiK5QSHDNSzl4Wh3\n/HE5O/Y2ZbE0IiIig59ChmtY2aGnztcfbOPOh1dksTQiIiKDn0KGq7w4v9P2vsY2DQAVERE5Cnq2\nuSvYzUPTfv30O+w70MrI4cVcc9E0AoGML/MuIiKSM9SScRhL19SybvsBFq/ezb4DvXl6vYiIiCQo\nZKS45qJpPR470KwZJyIiIl4oZKR430k1XHLWpG6PNTS1E43pEcQiIiK9pZCRIhgIcNLkqm6P/fLx\nVXz954vYtqfz1NZd+5q5+bev8/TizT6UUEREZPBQyEhzuLGd4bYodz++Krl9oKmNux5dxbY9TTy2\ncKMPpRMRERk8NLskzcSacqqHFVHXw0DP2vowkWiM2vowN//mdWIp01zj8bhmoIiIiLjUkpEmLxTk\nR18487Dn/OaZd3l4wfpOAQMgEj00ZiPcFmHz7kattSEiIkOWQkY3CvJDjBxe1OPx196ppSPSdRBo\nW8ehfbc/tJxbHniTpWtqM1JGERGRgU4howf//OFpnDipko/Pm9zt8ebWji772juigNNtsmlXI+As\n6AXQ1hFl9cZ9tLnniIiI5DqNyejBtImVTJtYybY9TTyxaFOX41truz5Ara0jSlO4g+Xr6pL7gu4Y\njYdeXMfLy3cy96QavnDx9MwVXEREZIDwHDKMMYXA3cBlQAtwh7X2Zz2ceypwDzADWA18xVr7Vsrx\nBqAcSIyWjAPl1toWr+XKlNKi3lfRhh2NLFq5k3XbDyT3xeJx4vE4Ly/fCcDi1bsVMkREZEjoS3fJ\n7cAs4BzgWuBmY8xl6ScZY0qAZ4CX3fOXAM8YY4rd42NxAsaxQI37Z8xAChgApUX5Rz7J9dtn3+0U\nMBJeXrGz07YGg4qIyFDgqSXDDQ7XABdaa1cAK4wxtwHXAY+lnf5PQIu19rvu9jeNMR8BPgXMB6YB\nu6y1W47mA2RaQf7RD1uZ/7zttN3Q1M6wsgLa2qMUFzr/CXbta+bl5Ts5ZnQZZ0wbTV6o6/uG2yIs\nXLGTaRMrOWZ0+VGXS0REJJO8dpfMdK9ZkrLvFeDGbs490z2W6lXgfTghYzqw1uP7+y4QCDB13DDW\n7zjAlz92Ivc+ueao7/nI39ezYr0zCPTGK2czeUwFP/n9WxxscQaTNrdG+IfTJnS57vFFG/nbm9sB\n+O33zjvqcoiIiGSS11/TxwB11tpIyr5aoMgYk74e9xhgZ9q+WmC8+3oaUGqMWWCM2WmMecYYc5zH\n8vjiW5fP5OZ/Pp3TTxjVL/dbsqaWlrYI0Vic196pZe22hmTAAPjD39YB8Pbavdx431KW2T0AyYAB\n6DkqIiIy4HkNGSVAW9q+xHZhL89NnHcCUAncAlwChIEXjTGlHsuUccWFeUysKScQCDC6srhf7/3X\nN7bxk9+/1WV/PB7nrsdWsXt/C798fDVvr9vb6Xhjc9cptCIiIgOJ1+6SVrqGicR2+oDNns5NnHch\nkJ8Y6GmMuQLYBnwUeKi3BQp1M3Yhk772yZN5dskWLjhtArc88EbG3uebd3Xuabrr0VWdttdtbyAQ\nCNDQ1MaaTfv57IWGmhElACxds5tgMMAZ00YTjcUIBfunjhJ17XedD2Wqc/+pzv2nOvefX3XtNWTs\nAKqNMUFrbaK9vgYIW2sbujm3Jm1fDbALwFrbASR/HbfWthljNgHjvBSooqJ/WxaOpLKylJknOB/r\n4rMm8/Srzhoa1cOLmVhTzrL39hz2+kAAqoYVE4vF2d/Y/fNRgE7dJ91JHxvynbsX84cffYTtew5y\n9+OrATimZjN79rdw0zVncvLUkQC8vmY3Ty7cwOc+Mg0zcQTxeJy7H11JXUOY7111OoX5ocNXAP7X\nuajOs0F17j/Vee7xGjKW4wSDOcBid988oLtf6ZcC303bdxZwK4AxZj1wi7V2vrtdChwHvOelQI2N\nYaLR7IxP+NjZk5Iho70jSvWwzkuRlxXn0xQ+FBZKivK4/atnUVacz466Zv7t3iX0p8dfWsu+lAe7\nbd19EIDv37OY4ycMp70jymZ337/+YhEPfv981mzaz/NLNgPwzML1nDtrfPL6WDyeXEwMnORbUVGc\nsTpfsb6OSDTGbNM/Y19yQabrXLpSnftPde6/RJ1nmqeQYa0NG2PmA/caY67GGcR5A3AVgDFmNHDA\nWtsKPAL82BjzX8B9wJdxxmk87N7uGeCHxpgtQB1O+NgKPOulTNFojEg3zxHxQ2pjU34owIdOn8DL\nb++gtT3KFy+ZzpzpNSxfX8e9T67mrBljuPzcqRTmh4hEYlRXdO5JKswPHfWS4395bSsHmtu7PbZ2\nW3pDE1z1f17stP3SWzuoKCng5ClV3P34auy2Bm68cjZVFUXsrGtme10Tb9q9jK0qYfOug0wYVcan\nzp3SL90xtftbuOOh5QB8/8rZTBk37KjvmUuy+e98qFKd+091nnv6sqz49Tgrfr4EHABustY+6R7b\nBfwzMN9ae9AYczHwK+CLwErgw9basHvut4F24PfAMOBF4CJr7aBZqSoQCHDK1GpWb9rHFy85kWFl\nhdx+7VwAStxFvE6ZWs3d3/oAwWDnR8CnfzH/4htns3t/mJt/+3qfy9NTwOitLbsP8vNHVnYKPDfe\nt5Q500ez9J1DD3pb4Ux+4d0t9Rw/YTizjh9J7f4W/vvxVZwytZpLzppMJBpj9ab9lBTlcfz4YWzc\n2Uj9wTaWr6/jzGmjOfX4kTyxaCOvrtrFtZfOYPe+Q0N6lq+vU8gQEckBgUG++mS8vr45q8k3Ho/T\nmrKolhe/fGwVy9bupWZECf/5xTmA00Xx7pZ6fvHIym6f9Npb1cOKqDvQ85iP/jJ+ZBl5oUCyGwag\nsryQ+oPpE4s6m3Pi6OQTaoeXFfCZC47n7iecsSSnTK3m6588OXOFHkTy8oJUVpaS7X/nQ4nq3H+q\nc/+5dR448plH+T6ZfoNcFwgE+hQwAK768AlMGTeMU4+rTu4LBgKcOGkEv/jGPNo6opQX59PY3M5r\n79Ty0EvrAfjeFbN46tVNnDd7PKMrS2iPRLnlgTc73furl85g/l/eY9Oug2TS9r1dHxR3pIABJAMG\nOCugvru1Prm9fH0d9QfbyM8L8tKy7Rw7roKTJh9ahqX+YBsPvbiOEyeP4P0zx9LSGuH2h96malgR\n1378JAKBjP9/IyIivaCWjEGirSPqfOGOrcAcU9nl+MoNddz58EoArvzg8Zw7azx76lv4+SMrqTvQ\nymlmJEtSvthPOGY4723tOk5joCjIC9Lu/ncNAN/41MlEY3FKCvN4cdl23rTOuiHfunwmDy/YkAw7\n//H50zlmdDn1B9tobY8wpqrrsisLV+ykKdzBh888xrm/G0ri8XingPLX17fy+nt7+MT7j6WitIBx\nI8sy+ZG7pd/w/Kc695/q3H9+tWQoZOSY9Bkhqa7+yUvJ17dfO5ffPPMuI4cXsXDFLr+Kl3Hfu2IW\nE0aV8Z17FhNui3LTVacxscZ5zktjczu19S38+H86L372jU+eTH5ekHufXENTuIP3nVjD6Mpinnhl\nU6fzbrxyNlN9HiuiH77+U537T3XuP3WXSJ/0FDDSjago4tufPhWgU8j42idmcNLkKt5au5df/dlZ\ni6NmRAm793dea+2SsybR0hbptNR5b4wcXsTehsyNFfnJ79/iMxccR3Ors/L9Dx94g2FlBZxxwmhe\neHNbt9f8/JGVnbaXrNnd7XlPLtrIVy+bwa+eXENNVQn/eN6RV8Fv64hy2/++RWF+iBv+6ZR+WxhN\nRGQwUEvGELJq4z6eWLSJj8+bzIxjD41xSG3hSH3wWqL7YOk7u7nvz+90utcvvjGPsuJ8YvE4dQda\n+fVTa9iwozHZtRGJxnl68eZOA0KPHVvBDz53Gm+8t4d73EGek2rK2bWvpcfpu6VFecnAkG3HjCqj\nPRJLBq6PzJnI+06qYVx1KU3hDh5buJG8UICmcAcBApw/ezwbdh5IPosG4NJ5k/noWZOP+F7b9zQR\nCAaYWFPe7W94W2sPsmJ9HefOGk9ZcX7/f9ghTL9V+0917j91l/SOQkY/eGbJZh59eSMXzB7PZ/7h\n+C7HY/E4Ty5yug7WbK7nhInD+eQHpnQ5ryMSoyncQWW5swZIU7iD//rTcjbtOkhZcT43/OMpya6L\npnAHoWCAooIQW2oPdhm4mnDjlbN5evFmVm7Y10+ftv9981MzeXzhRrbUdh1kO7a6lJ11zV3OP3mK\nE/LqGsLk5wV5e10dy9fX8bkLDa+u3s3jCzeSFwpy27VzaWqLUloQpLLs0NoqX/i/C4jF45x6XDXj\nRpZy8rHVTB1/+K6cSDTGvgOtbNh5gNNPGE1+nlpVuqMvPP+pzv2nkNE7Chn9IB6Ps7chTPXw4sN2\ntxzND4LDjRUBZ7Gwh/++ng+dMZE/vLiW/Y3ODJXbr53LiIoitu1pOqo1RFJ9/8rZvLx8J0vW7CYa\ny86//89/5AQeW7iRA03t5OcFez1duSAvyMfmTebhBRu6HLv83Kl8yB3MChCLxZPrszQ0tXHzb19P\nLlf/yXOm8JE5E7t9j7++vpXtdc189h+OpyBlmfm12xq458nVnHfquF61xgxW+sLzn+rcfwoZvaOQ\n4SO/fhC8snIXv332XQDu+/Y55LkP8mkKd7Bk9W7++sY2Tj2umovmTuKXj6+iqCDE3JNqWLqmlmkT\nK2lujfCBmWP5wf2vEY3FiaQsUzxtYmVyLEo0FuNfbvt7j+W4dN5kHl+0qcfjA9HY6lLMMcPpiMRY\nZvdwmhnF5z8yjYdeXMdf3+g8JuWub87jzff2MOPYKuoPtvHU4s1UDyvipbd2AHDZ+4/l4rmTiMZi\nBAMBrrtzEeE2p+sqtVvtSI4UMAcafeH5T3XuP4WM3lHI8JFfPwhi8Tivv1vL2KpSjhld3uf77G9s\nJRaL886Weh547j0CAfjRF87sNK110cqd/O5Z53E5v/zW+/nqfy1MHvv518/mG794pct9B5s7v3Z2\nl6f69tbH503mhTe2Mba6lHXbDyT33/aV91FVUZSc8rtrXzO797VwMNxBc6szQ6cgL8hdj65yntPz\n2Vmd6r2ltYP9B9uoGVFCbX2YovwQVWnP/skWfeH5T3XuP80ukSErGAgwZ3r6A3y9G1HhfGm9f3gx\n7585tttzzp4xhrLifGpGlHRZVK00ZUDlB0+fwIxjq7j7idXJ3+bBWfH00nmTueuxVYCzkunE0eUM\nKyvoMlg2W/oaMACecFtyUgMGwHfucR7ud/u1c9l/sI3b/vctItFDv7Ckd+csX1eXDBnhtghfu3MR\n6b/eXDx3ImfNGMOw0oLk0vbL7F5mTKmivDift9bWUTOiuM/rlextCLPgrR3MnVHD+CyseSIyFKkl\nQ3ptKPy2sczu4U8L1vOJD0zhjGmjWb6ujnU7Grhk7mQKC5zxCU8s2sifX90MHOrOSUx7fd+JTjiK\nx+P85pl32dMQ5rrLZtDU0sGqjftobY/y5CubKC3KoyMao70jxqjhxZw8pYoddc3s3t/ChWccw9/e\n3JZcFv780ycwrqqE+c/bLuW9eO4k8vOCzJk+mtv+9232NWZ+Kfl0k2rKO80i6s4HT5/A6SeMYvf+\nFn7zzLtHvOeJkyqpKC1ILiA3fmQZ2/c2UVQQ4sdfnMMwdxBsJBrjz69uYsfeZj5zwfGdWkMaW9op\nzAtRWBAiHo/zg/tfY5f7jJzvf242U8YOo6GpjacXb2b28SOZNmkE0PnfeUdHlEAgQCweJwBaTTZD\nhsLPloFG3SW9o5DhI/0gcESiMZ5duoVJNeWcPKX6yBekXbtifR2Tx1TQ1hElFodx1V1XJU1IrfNn\nl2zhoRfXdTqeOjYiEo3xxZ/+/bDvf86p4wgEYIE77mKwuvbjJ/HM0i1sSQs3E0aVsW1PE8eNH9al\n9SXdly45keeWbmHrHme12N9891wCgQBrtzXwwrLtLHtvDwDjRpZysLmdYWWF/OBzs2loamdvQ5hp\nEyuJRGNsrW2iorSATbsamW1G9nktlEg0lhx/NNToZ4v/FDJ6RyHDR/pB4L/0Oq8/2Mbi1bt4avFm\nrv7INM6YNrrT+Tvrmvnbsu1MGVvBivV1yeXXE+779jnE43GeW7q104qmeaEAw8sKOW/WeP60YL0v\nn20w6s1y/NdcNI2zZozhhTe38cSijVx5oeHMaaNpaGrn8YUbmT6pkm17mygryufcWeMoKsjjgefe\nY9GKncw2I/nEOVPYVtvE6+/tYUR5IQ1NbXzuwhMoKTrUnXegqY1YnOR0cXBaz6KxOEtW72bSmAom\njBo8XUL62eI/hYzeUcjwkX4Q+K+nOk+dntqTpnAHv3h0Jevd3+i/cPE05p40Jnm8sbmd1o4oo4YX\nd3puSyweZ5ndm1wwLdWnLziO82eNZ/5fLAtX7EzuDwUD3PaVuSxfX8f/+4vTrXPjZ2dzz5Orj/jA\nPL+eGDxQnTtrXKeWpQB0Ga8CzkDkltYI//34KnbsddZe+ciciXx07iReens7f35lc3JRu/y8IJef\nO5WqYUWs2bSfeDzO9EkjOOW4anbta+GpVzcx24zixEmV5OeFuqyZEm6L8MjLGxhdWcIHT5+Q3N/W\nEWXTzkamjBvW5Zra/S28afdw9owxye6spnAHkWiM4SlrvHRHP1v8p5DROwoZPtIPAv8dbZ3H43H+\ntGA9zeEIV33YeG7Kr93fQkF+iIrS/C7X/uX1rTy2cCPlJflcctZk3j9zLLFYnD/8bR0lRXlc+v5j\nWfDWdv7fX9d2ue8lZ01Kjmu5/vKZnHRsFWs27ycei9MRiTFl/DAqSgr4/q+XsmtfC5XlhXzs7Mk8\n8Nx7nutAemdMVQmfOncqp0yt5tdPrUmOh/mn86byD6dPYNueJp5evDnZOnbK1GqKC0PYbQ0EA4Fk\nUDzNjOQrHz+JddsPcOfDK4hE49xyzRmMrixmT32YLbUHOc2MIhgMJMNt6r/zzTsbORju4GBLOwX5\nIU6ZWk0kGiMai1OYsm5LQrgtwt/f3sGMY6sY34fWm0QZ2tqj5OcFjxjec4VCRu8oZPhIIcN/g73O\nY/E467Y1MG5kGeu2N/DMki184gNTqBlRwoPPv8cpU6s559RxPV5fW9/C4lW7mXfyGKqHF7Ozrpnt\ne5uoLC+kKdzBXY+u6nLNx+dN5sIzjmHF+jrW7zjAnOk1/Gi+s6JseUk+t1x9Bvc8sZqaqhJOmTqS\nzbsbKS8p4OEF65NP/vWiqMCZfptoXRA4afIIVm/a32lfQb6z6FziK+fj8yYnZy995zOnMq5mGIuX\nb+fRlzd2WtvmQ2ccwwtvbiMQgO9dMZvCghAB4N0t9UyfVMn85y12WwPjR5Zxwz/OJBAMsHFHI6XF\neUwZO6zb0PD6u7Vs2NHIBaeN565HV7KnIUx7R4wJo8q46arTCAYC/OrPawi3R7ju0hkU5IfY39hK\nbX2YE44Z3uMA4Eg0RigYoLk1wntb6pk5tZr8vCCNLe28/PYOzpw+mlGVJTQ0tXHPE6uZPmkEHzv7\n8AvbRWOxLgF/T0OYwrxgssWot7bWHuT517dywewJHH/McIWMXlDI8NFg/8IbjFTnh3fXoyt5e10d\nl587ldKiPDbvPsjl503t8htvc2sHr6zcxclTqjqt15Hu3idX8/q7e7j2kzOJdkTYtLORTbsaqT/Y\nxjGjy3lr7V6qhxXxn1+c02WQZv3BNv73hbWMG1nKR8+aRO3+MGXF+fz97R3J8S/nzRpH/cE29jaE\n2d5DKLlg9ng27z7I+h2HH7janbLifJrCHV32j6su5dixFSxamTtPXO5OXijQaSo1ODOVttQ2dVsv\n3bnofROZMm4Yv3AfnPhP501l/8G2TovZjRtZymlmFOfNGsffl+/k1ZW7KC/JZ8POxi73mzymgsL8\nYHIsz4iKQkaUFyX/+37h4mkU5IVYtnYvJUV5XHLWZIaVFrB49S7uf/rdZJkA2jtiVFUU8tBL6wkF\nA3z3M7NoaGrj5ClVNLdGKC3K67RK79bag7R3xDgYbmdSTQU33f8aLW0R8kIBfvtv5ytk9IJCho/0\nhec/1fnhtbZH2L63mWPHVPRLM3c0FqOxpYPjJ1d3W+d1B8KUFuV3WVPlSBau2MnabQ185oLjKCly\n1l+JxePE43FeXr6T9TsOcOa00eTlBZk+sbLTb8rxeJzd+1sIBAK89k4tleWFzDt5DOu2H2BEeSFv\n2D08sWgToyqLuelzp/HlO17u8v4/+sKZjK0uZff+Fu56dGVyKm+qD5wylldX7Up+SVdVFBGJxjjQ\n3O7ps8rRO2PaKF5/d0+fri0vyScSjXPylCpee6e2x/MeuPF8qqrKFDKOQCHDR/rC85/q3H+Dsc5j\nsTgEnIXsEk85Ps2M5Ixpo4kDp58wqsv5//k/y9i4s5HJYyr4wedmEwgEeHdLPX/421qmjhvGpy84\nnvy8IItW7OR3z73H+JFl/OunT6HEDVj/9qul7Gts5Ysfnc74UWWs3LCPR/7uLMJWkB/k+1eexq59\nzdz75Jpuy5w6wHXO9NFs29vMjr1NnDyliplTqnjT7uXdLfUAjB9Z2mPLj/TN8ROGc8c3P6CQcQQK\nGT4ajD98BzvVuf9yoc4PNLdTXpJ/2GfGtLZHeHdzPdMnj+h2QGVCPB5nT32YqmFFnbqI9h1oZff+\nFqZPclpeorEYj728kThOq8joyhIAVqyv4+du10NleSE3fnY2IyoKCQScgZ/xOBQUhLrUeVtHlN89\n+y6V5YVcfu5UWtuj7Kxr5pjR5WzYcYDq4UUMLyvkYEsHP5r/ZqdZTPl5Qb50yYksWuG0Ek0YVcb5\ns8fz8oqdrN64n2FlBXz+wyew70ArBfkhjhldzl/f2Mo7m+u7zIZKtBxNHT+MP720gVGVxYypKuG5\npVuJpX1/XnLWJE4zo3jT7uFvb26nJWV1YIArLzTJ2VcJgQB4+Rr2ev7hPHXHxxQyjkAhw0e58MN3\nsFGd+0917r+jrfPW9gi/fuod3l5Xx/tnjuGqD53Q7eDMto4o+xtbDzsu5+XlO5yxPedOPWy32LY9\nTXSRb5oAAApDSURBVOSFAsRicZasqeW48cM4eUpV8n07IjH21LeQnx9i/4FWTphYSTwe539eWMuC\nt3ZwyVmT+NjZk5Pnv7pqV6fVcAMBuOWaM7np/teS+/77m++nID/IYws38vxrWwHICwX5v19+H5Xl\nhcRicezWen733Hs0t0aoKMnn/7d398FWVWUcx783jIsohoEJDqZj6JOaGVRAAoZok5ovSKagmYaR\nio6jTpPjWxRmTviSyiRFpOaUjlqjODFpmo5yHW6K+BKD84jvgygTFErxIgL98awDm+O93XPOPWff\nu+/8PjMM7LX2ubPOczbrPmfttdbea+AunHbUAWzavIXL57Tu8B6UZHRMSUaO1PnmTzHPn2Kev3rE\nfOOmzSxbvob9h/T/vyMz3dWHm7dw16PL2KN/H44e8eltyce9j73CQ0+/xcnjPsOxo2IC6MZNm/nH\nq6tp7t2LT+zSu6oHSba8+A477dTEqIMHscfAfkoyOqAkI0fqfPOnmOdPMc+fYt6+rVu3suY/H9B/\n1951fXZOt30Kq5k1A7cCE4F1wA3ufmM75w4DZgOHAEuA89x9caZ+MnA1MBh4GJjq7qurbZOIiEhP\n1NTUtMP28UVTy9N4rgeGA+OAacB0M5tYfpKZ9QXmA0+k8xcC881s51Q/ApgLTAdGArsDd9TQHhER\nEemGqkoyUuJwNnChu7/g7vOAmcAFbZw+CVjn7pd6uAhYC3wr1Z8P3OPuf3D3JcAZwLFmtk+tb0ZE\nRES6j2pHMg4lbrEszJS1ECMR5UamuqyngK+kf48CnixVuPty4K1ULiIiIgVXbZIxGFjl7tnFvyuB\nPmY2oI1zV5SVrQSGVFgvIiIiBVbtxM++QPlzm0vH5TNT2ju3ucL6ivTqVcu0EqlFKdaKeX4U8/wp\n5vlTzPOXV6yrTTI28NEkoHRcvhl+e+euq7C+Ek277bZzFadLPSjm+VPM86eY508x73mqTWXeBgaa\nWfZ1g4D17r6mjXMHlZUNAt6psF5EREQKrNok43lgEztOzhwLPNPGua3AYWVlo9k+abQVGFOqMLO9\nifkYrYiIiEjhVb3jp5nNJpKFKURScAdwprvPM7M9gffcfYOZ9QOWAXcDc4BzgZOBoe6+3sxGAY8T\nS1kXATel155Ul3cmIiIiXaqWmR+XAM8CjwGzgKvSfhkQtzpOAXD3tcBxwOFEEjECOMbd16f6VuAc\nYjOuFmA1kbiIiIhID1D0Z5eIiIhIN6X1QiIiItIQSjJERESkIZRkiIiISEMoyRAREZGGUJIhIiIi\nDVHttuLdgpk1A7cCE4ltyG9w9xu7tlXFZmZ7AbcARxAxvRe4zN0/MLN9gd8QT9B9A7jY3R/JvPYo\n4BfAfsRma1Pd/fVc30CBmdl8YKW7T0nHw4DZwCHAEuA8d1+cOX8ycDXxkMGHiXivzr3hBWRmvYlr\ndTLxrKTb3P2KVKe4N4CZDSHiejixVcHN7n5zqlPM6yj9blwEnO/uT6ayfelE/21mFwE/APoB9wEX\nuPuGSttU1JGM64HhwDhgGjDdzCZ2aYuK709AH2KjtUnA8cR/boB5xBNzvwj8Hrg/dRylnVrvB34L\nfAlYBTyQa8sLzMwmAcdkjvsC84EniGt8ITDfzHZO9SOAucT+MiOB3YkN8aQytwBHAl8DTgOmmtlU\nxb2h7gPWEnG9CLjGzE5UzOsrJRh3AweVVT1Ajf23mX0T+BEwFRhP7PY9s5p2FW6fjHRhrgK+7u4L\nUtkVwJHuPr5LG1dQZmbAUmBPd1+VyiYB1wHfIZKMT5WyVzN7BFjg7jPMbAYwphT71EG8CxxfyqSl\nbWa2O/AC0QEsdfcpZjYFuNzdh2bOexn4qbvfaWa/AzZnRj2GAG8C+7n7m/m/i+JI8V4JjHf3llT2\nQ+AA4CngCsW9vsysP/Av4HPuvjSV/ZG45p9DMa8LMzsQuCsdfh44wt2fNLPxRNJQU/9tZk8Aj7r7\n1al+NPBXYECloxlFHMk4lLjNszBT1kJkulKbd4GjSwlGRjORuS4uu6BaiKE3iLhvSybSjq6LM/XS\nvuuBO4GXMmUjifhmPcX2eI5ix3gvB95ix+cJSdvGAGtKCQaAu8909+8R8VPc62898F/gu2a2U/pC\ncxiRYCjm9TMWeIiIXVOmfCQ19t/pQahfBhZkXtsK9CZ+D1ekiEnGYGCVu3+YKVsJ9DGzAV3UpkJz\n9/fK7tE1ARcQF9dg4ltH1kriuTVUUC9tSN8wxrL9llSJ4t04+wFvmNkZZvaSmb1qZlem611xbwB3\n30j0JecSCcdLwF/c/XYU87px9znuflkbowudiXF/4hb6tnp330zMq6n4MyjixM++xIStrNJxc85t\n6amuA4YRWewltB3vUqzb+zz0WbQj3Tv9FTDN3TfGl7ttOoqn4l27XYlbI98HziI62F8TE50V98Y5\nEHiQGLk7BJhlZn9DMc9DZ2LcN3Pc3us7VMQkYwMffYOl43U5t6XHMbOfAxcCp7j7UjPbAHyy7LRm\ntse6vc/j3w1taLH9GHjG3R9to669eHYUb137HfuQmCE/OQ29Y2b7EJPHX0ZxrzszOxI4GxiSRjWe\nS3MrrgReRTFvtM703xsyx+29vkNFvF3yNjAw3S8qGQSsd/c1XdSmHsHMZgEXA6e7e2mG8dtEfLMG\nEU/craRePupUYIKZrTWztcDpwLfN7H1gOYp3o7wDbColGIkDe6PrvFGGA6+lBKPkOWAfFPM8dCbG\nq4lEY1u9mfUCBlDFZ1DEJON5YBM7Tv4ZCzzTNc3pGcxsOjGMfKq735epagWGpyH+kjGpvFQ/JvNz\n+hK3WlqR9nyVGDY+NP15kFjB8wXg78TEuKzRbJ/oXB7vvYn7o4p3x1qBj5tZdonfQcDrqW502fmK\ne+etIBawZUfNDwReQzHPQ63990J330r8Xh2Tee1hwAfEqriKFG4JK4CZzSYuxinERXcHcKa7z+vK\ndhVVWv70IvAzYpOzrH8SF9QSYpLiCcBlwMHuvjwNNy8FfgL8mVjTvr+7D8+p+YVnZrcDW9MS1n7A\nMmK9+xxiwtzJwFB3X29mo4DHgfOJTXduAt5z95O6pvXFYmYPEsPH04g5GXcCM9LfrxDLABX3OjGz\n3YjJno8A1wCfBW4j+pB7UMzrzsy2AOPSEtSPUX3/fYC7D0s/61Ri/thZRMJ4G7Gk9eJK21PEkQyI\nyYjPAo8Bs4CrlGB0ygnEtXAlcSGtIIbDVrj7FmACMWS2iNjAaEJpyDmtV59IJHxPEzOS1QnUyN3X\nAscRuyMuAkYAx6SlZbh7K3AO0Rm0EEOaU7qmtYV0OvGLbQHx5eQWd/9livs3UNzryt3fJzY/G0z0\nDzcAM9x9rmLeMNtGDlL/fSLV9d8TMq+/B7iWmCD9MDHKdGk1jSnkSIaIiIh0f0UdyRAREZFuTkmG\niIiINISSDBEREWkIJRkiIiLSEEoyREREpCGUZIiIiEhDKMkQERGRhlCSISIiIg2hJENEREQaQkmG\niIiINISSDBEREWmI/wHyZ3A9sSzgfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fce125d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the training lossses\n",
    "plt.plot(losses)\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EZ599wt69e/j004/55ZefGTr0esB1u3/+/B/YvHkTqakbmTx5Yrmve9llV1JcXOSZffC/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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fce13080f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ploter_d0(D, input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save the pretrained parameters to reuse\n",
    "D_pre_params = sess.run(param_D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct GAN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# G Model\n",
    "with tf.variable_scope(\"G\"):\n",
    "    z_tensor = tf.placeholder(tf.float32, shape=[batch_size, 1])\n",
    "    G, param_G = mlp(z_tensor, 1)\n",
    "    G = tf.mul(G, 5.0)  # scaling\n",
    "# D model to take x as input\n",
    "with tf.variable_scope(\"D\", reuse=None):\n",
    "    x_tensor = tf.placeholder(tf.float32, shape=[batch_size, 1])\n",
    "    D1, param_D = mlp(x_tensor, 1)\n",
    "    # Clips values to a specified min and max\n",
    "    D1 = tf.clip_by_value(D1, 0.01, 0.99)\n",
    "# D model to take D(z) as input\n",
    "with tf.variable_scope(\"D\", reuse=True):\n",
    "    D2, param_D = mlp(G, 1)\n",
    "    D2 = tf.clip_by_value(D2, 0.01, 0.99)\n",
    "# The objective function of G and D model\n",
    "obj_D = tf.reduce_mean(tf.log(D1) + tf.log(1-D2))\n",
    "obj_G = tf.reduce_mean(tf.log(D2))\n",
    "# The train operations of G and D model\n",
    "train_op_D = momentum_optimizer(1.0 - obj_D, param_D)\n",
    "train_op_G = momentum_optimizer(1.0 - obj_G, param_G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "tf.set_random_seed(1234)\n",
    "# Load the pre-trained parameters\n",
    "for v, p in zip(D_pre_params, param_D):\n",
    "    sess.run(tf.assign(p, v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The ploter \n",
    "def ploter_g_d():\n",
    "    \"\"\"Plot the p_data, p_G and decision boundary\"\"\"\n",
    "    f, ax = plt.subplots(1)\n",
    "    # p_data\n",
    "    xs = np.linspace(-5, 5, 1000)\n",
    "    ax.plot(xs, norm.pdf(xs, loc=mu, scale=sigma), label=\"p_data\")\n",
    "    # decision boundary\n",
    "    r = 5000\n",
    "    xs = np.linspace(-5, 5, r)\n",
    "    ds = np.zeros(r)\n",
    "    for i in range(r//batch_size):\n",
    "        x = np.reshape(xs[batch_size*i:batch_size*(i+1)], [batch_size, 1])\n",
    "        ds[batch_size*i:batch_size*(i+1)] = np.reshape(sess.run(D1, feed_dict={x_tensor:x}), [-1])\n",
    "    ax.plot(xs, ds, label=\"decision boundary\")\n",
    "    \n",
    "    # Plot the generated distribution by G model\n",
    "    zs = np.linspace(-5, 5, r)\n",
    "    gs = np.zeros(r)\n",
    "    for i in range(r//batch_size):\n",
    "        z = np.reshape(zs[batch_size*i:batch_size*(i+1)], [batch_size, 1])\n",
    "        gs[batch_size*i:batch_size*(i+1)] = np.reshape(sess.run(G, feed_dict={\n",
    "                    z_tensor:z }), [-1])\n",
    "    histc, _ = np.histogram(gs, bins=20, density=True)\n",
    "    ax.plot(np.linspace(-5, 5, 20), histc, label=\"p_g\")\n",
    "    \n",
    "    ax.set_ylim(0.0, 1.2)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1fce5dbe780>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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soEBEmqK8vPw9Fi6SXVkeT2AjvGRMNFSdgjA5VOpYRCTluMvLwZebkYyJhgoK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\nY0ddOef2PYNDOxwUyebFjYKCGAuOSF3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YXlbLsjUHmFaUTv4IYaYFca/1le76rcHVyB+2\nvERFm56A6/zsc7hrws04W3w46bg862tpgUDJeo/Zdsp4MFQI9llv6ZVSoCiKU5blN4AXZFm+F8gB\nngEWQmipoElRFBfwKyAfPZ+BKfAZ6FaFHrvc+/0qPt8Q/PHEtc9WPY2N/f43GtlvXtWnJyZq0PMQ\nzMyawc1jbsDv19BXkSJDU5uHt7/YB8DwVDvzzs87bZ8M2XttgBifNpYax/fsqd2Hx+vr4CPyoyvG\nUnq4AbfHzysf7+EfF56D2TQ0zbR9QdxrfaOzfqty1LB42xIa3Lqx+tLcOdxYdC2aKuFTT+1jd0O7\nUVuKTxD/h5Poy6/0aWAL+rLAYvQshcsCn1Wg5yEAPeOhHdgAnAjb/vdMBB4qmBMSIGBGH0oRCH7V\nz2u736G0Xn8Yz8gsjmjYYRBN03jrc4VWpxeAhVeNw2oRa9v9yfj0sYDuSHqs5USHz9KSbCy4SHfs\nOlLVypebjkVcPsHQ52jLcX6/5bmQQnBdwVUsKJp32vEmPIusyFNwKr1Oc6woihO4J7Cd/JkpbF/k\njjwNksmEOSEBf0vLkKmUqGkaf1E+DNUymDJsIgsn3BZxhQBgY2k1WwLLBpdOH8m4vNSIyzDUGZdW\nhCRJaJrG3voyRiXldPj8suk5rN9dyaGKFv723UFmyBlkpIi1dEH/sL/xUKjUuoTErfINzBl5frfn\nhY+3IvrgVIQ9z0BCCYyGSP2Djw9+zrqKjYBey+DeiXdgNkV+dt7U5uHtL3VLxbBk21mXyjhSxFnt\nFKWNBghZhsIxmSQWXjUOkyTh8aq8+bkiUiAL+oW99WX8afvLuPwuzJKZeybe3iOFAE6yFCR07nR8\nNiOUAgMZSvUPVh/9LhQXnJOQzYNTFmI1Rz6FsKZpvBm2bHDvNeOxxYhioAPFlOG6QfBgUzku38kp\nTGDU8ESumjkKgF2H6lm/uyqi8gmGHnvqFF7Y8Rpe1YvVZOHBKXczY3jPqxwGK9Oa7HZM1rMzzfnp\nEEqBgQwVpWBz5TbeL/sIgGG2NB4pXoTdYoyZeENpFVvFskHEmDZiIgA+zc+u2j2dtpk/ezSZgWWD\nt7/cR32zK2LyCYYWO2r28OKO1/GqPmJMVh6eci8T0+XuTwwjaCkQ/gSdI5QCA7EEUx0P4qJIpXX7\neKNUj1dPjEngsan3kxxrTD7xxlZ3KNpALBtEhjHp+aTbdMVrc/X2TtvEWM3ce+14JMDh9vHKJ6Wo\nYhlB0Es2HtvOC9v/jE/zE2uO4ZHiRchpRb3+nqBPgfAn6ByhFBhI8Kb0DdLog8PNR3lp1xv4NT82\ncyyPFi8iIy69+xMHAFXTeOWTUtpcekyyWDaIDJIkMSOrGIA9dftodDd12m5sbgpXzdKXEUoPN7By\ns4hGEPSczZXb+f26JYGxxsZjU+/rkLa4NwQtBRahFHSKUAoMJOhoqLldqO5T12OjmTpnPc+XvIYn\nUH3swSkLyU0caZg8KzcfY/ehegCuPC9XLBtEkNkjz0NCwq/5WXXk2y7b3TingNxM3bHr/a8PcLym\nNVIiCgYxGyu38vKOt1E1lTiLncen3UdB8ug+f1+wMm1w/BV0RCgFBhJuvhpMYYkOr5PnSl6lxasP\n6gsn3s7Y1N6b8fqLo9WtvP/1fgByMxNYcJFYNogkWfGZFGfoqYy/PraWIy2dWwEsZhMPXDcBi9mE\nz6+yZPkefCKrn+A0bK7azht7/oqGRkJMPE+d8yCjk0ad0XcG88IEK9UKOiKUAgMJL9vpHyRhiT7V\nx5Kdb1Dp0NOJ3lB4DdMzpxgmj8fr56Xlu/H5NawWEw/Mn4h1iBY4iWbmF16F1WTFr/lZvG0Jyw9+\nzpaq7RxsOkyjuwlV0x/+IzMSQr4eR6pb+TBQqEogOJlt1Tv5856/oKERb43j15c8eUoujN6i+Xyo\nDj2tsfAp6Byx6Gog4TelbxA4G2qaxjt7l7KvUU9ffOHIWVw+aq6hMulmaP1HfuulRYwcJoqbGMHw\nuAzuGHcTb+z5Kw6fk8/Kv+rweWpsClfnX8YFI87j8nNyKNlfS+nhBj5bf4Txo1KZVGCML4ogOtlR\ns5tXd7cvGTw140HyUnLOuE6Bv7V9yUr4FHSOmFIZSPia1mAIS1xRvpINlVsAmJAuc8uY6yNa8fBk\ntu6rYeUW3VQ9pTCdS6YZ59MggPOypvPEtAcYm1KIxdRxvtHgbuSdvUv1mZ+mct+8CSTYrWjAS8v3\n0NAyuHxqBAPHrtpSXt71FqqmhpwKc5P657cdPs4Kn4LOEZYCAzHFxYHZDH5/1Nc/WF+xmRWHvgT0\n5ESLJt5pSLbCINWNTl75pBSA5IQY7rlmvKEKikBnTGohT6QWomoqTe5mGtyNVLXVsPLIN1Q6qtlU\ntY0Ys5Xb5Zt44LoJ/P69ElqdXl5ctou/u2OaKJp0llNav48lu97EHwg7fGzqIvKScvvt+32i7kG3\niF+ggUiSFNJWo9lSsK9hP+/sXQpASmwyDxffg81iM0wer8/Pcx/uxOn2YZIkHpo/keT4mO5PFEQM\nk2Qi1ZZCQfJozs8+l5+d+xPGpOghZGtPbGRT1TYmFaRz7fl5AOw71sTfvj1kpMgCg9nXsJ8Xd7yO\nL5CY6JHiReQn5/XrNTpaCoRS0BlCKTAYSyirYXRaCirbqnhpZ3sugkeK7yUlNtlQmd5dWcaRKn1t\ncMHcAuRRIvww2ok1x/DglIUMs6UB8FflbzS5W7hhTj5jc1MA+OT7w+w8WGekmAKDONBYzvMlr+FV\nfVhNVh4uvpeilPx+v05onJUkUfegC4RSYDBBE5YvCi0FrZ42ni95DafPhUkysWjSjxiZMMJQmdbt\nquDr7XqZ3uLC9FBefUH0Y7fYWTjxdiQkXH4XHx/8DLPJxIPzJ5IYp+egf+mj3VQ3OAyWVBBJjrYc\n57mSV/GoXiwmCw9NuZuxqQMTVhxUCswJCUhiqapTRK8YjDkhsHwQZSGJPtXHy7vepNalJwS6ZewN\nTOhljvH+5sCJJl7/VAEgPcnGonkTMAk/gkFFQXIeM0fMAOD7is0cb60gNTGWB+ZPRJKgzeVj8VJ9\naUgw9Klqqw5VOzRJJu6fdBfj0sYM2PWCPgXCybBrhFJgMEFLQTQtH2iaxnv7llHWqMeQX5wzmzkj\nZxkqU0OLmz8t3YnPrxJjNfH4TZNJsIsKZ4OR+QV6TgMNjc/L9cqaE0enceslegKs47VtvPzxHlEf\nYYhT72pg8faXafW2ISFx94TbmDRs/IBe0x/KZij8CbpCKAUGYwlzNIyWWvNfH1vL2hMbABifNpYF\nRfMMlcfj9bN46Q6a2jwA3HftBEYNF5r+YCU5NonZ2ecBsLV6B9UOvarlFefmcsGkLAC2ldWyTDge\nDllaPK0s3r6EBncjALfJN/aq/HFfEUpB9wilwGCClgLN50N1GV9StrRuH0vLlgN6Qpp7DQ49VDWN\nV1eUUl6pW1Lmzx7NOeMyDZNH0D9cPmouZsmMhsbKI2sAPRpn4VUy+SP038TydeV8v6vSSDEFA4DT\n5+TZ7S9T7agF4PrCq7kwQpbIoEXWIlIcd4lQCgymQ/0Dg/0KKtuqeWX3W2hoxFnsPDTlbuKsdkNl\nev/rA2ws1VMqz5AzmH9h/3skCyJPqi2Fc4dPA2BT5VYcXicAVouZxxZMJiVBDzF9dUVpqNCVYPDj\n8Xt4vuQ1jrbqzsJXjLqYH+RdErHrByvSCktB1wilwGCiJathm9fBCzvaIw3um3QXmXEZhskD8MWm\no3y24QgA+SMSue9a4Vg4lLgo53wAPKo3lCkTIDUxlqdumYo91oxf1fjThzs5XBk9PjeCvuFTfSzZ\n9SYHmsoBmJ09k+sLr47Y9VW3G82tW2OFo2HXCKXAYMLzbxulFPhVP6/seosapx4jfvOY65HTjKt6\nCLCxtIq/fFUGQGaqnSduLiY2xrhlDEH/k5eUS16inq3u2+PrO/jU5GYm8NiNkzGbJNweP//7fyXU\nNjqNElVwhqiayht7/sqeOj16aEZmMbfJN0Y0C2l4JVphKegaoRQYTHiqzdZtW/E7zqzgR194v+wj\nlAa99PBFI88PzeCMomR/LUuW7wEgKT6Gp2+dSlKcyFg4FJkTuNeqHNWhaJcg40ensWie7o3e1Obh\nv/+yXdRIGIRomsbSsuVsqS4B9LopP55wKyYpco8fzeejZcP60HuLSHHcJUIpMBhTbCzmZD1DYPO6\ntRz6+59Su+zDDtW8BpI1x9ax5vj3AMipRfxwzPyIXLcrdh2s49kPd+JXNWwxZp66uZjMFGP9GgQD\nx4zMYmxmPWX2hootp3w+a0IWtwRCFasbnfz23W00tQrFYDDx1dE1fH1sLQD5SXncP+muUwpmDRSq\n10vj6lUc+uXPqP3g/dBxa4axS6PRjFAKooCRTzxN3PiJAKhOJ/XLl3Ho5z+l9oP3BzR/wd76Mv6v\n7CMAMu3DuG/SjwyNNNhTXs/iD3bi82vEWs08dUsxeVli7W8oE2O2Mj1zCgDbanbg9ntOaXPVzFHc\nMEd3MK2sd/Dbv2yn2XFqO0H0salyGx/u/wTQo5keKr6bGPPAW/1Uj4eGlV9S/sufUf32G/jqdWfV\nmJE5ZP/kSSwpIjV6V4gqiVGAbVQeOc/8Hc4D+6lb/hGOXTtQXS7qV3xMw1dfknLxpaT+4Cosyf1X\nc6DaUcMrgfKk9lCkQVy/fX9v2Xmwjmc/2InXpxJjMfHkzVMYk5NimDyCyDFzxAzWVWzE7fdQUrOL\n87Kmn9Jm/ux8fH6Nj9eVc6K2jd++s42nb51KamKsARILesLe+jLeLH0PgKSYRB4tXkSCNX5Ar6m6\n3TR9s5r6z1Z0iOaKHZVH2rz5JEydJtIbd4NQCqIIe2EROU8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wVlWi1lTRdvRYhzKs/pZm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\nT5d7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fce4d65a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Before training\n",
    "ploter_g_d()\n",
    "plt.title(\"Before Training\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished\n"
     ]
    }
   ],
   "source": [
    "# Train the GAN\n",
    "np.random.seed(2017)\n",
    "k = 1 # The training steps of D\n",
    "# Keep track of the objectives\n",
    "fs_D, fs_G = np.zeros(train_epochs), np.zeros(train_epochs)\n",
    "for i in range(train_epochs):\n",
    "    # Train D\n",
    "    for j in range(k):\n",
    "        x = np.random.normal(mu, sigma, size=batch_size)\n",
    "        x.sort()\n",
    "        z = np.linspace(-5, 5, batch_size) + np.random.random(batch_size)*0.01\n",
    "        fs, _ = sess.run([obj_D, train_op_D], feed_dict={x_tensor: np.reshape(x, [batch_size, 1]),\n",
    "                                             z_tensor: np.reshape(z, [batch_size, 1])})\n",
    "    fs_D[i] = fs    \n",
    "    # Train G\n",
    "    z = np.linspace(-5, 5, batch_size) + np.random.random(batch_size)*0.01\n",
    "    fs_G[i], _ = sess.run([obj_G, train_op_G], feed_dict={z_tensor: np.reshape(z, [batch_size, 1])})\n",
    "print(\"Finished\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fce5e27ac8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ddsbHj+Onc3/M6sp1vLrrLQCMOgM3zLoaS1Lwu1RK28r5y/rHcWseIvQR/P6Y\nX/Lm7vdYW70RgGfOfABPpy6gzTVN4+P9n/tT2TpFx82zr+1zjIr2Kv68/rGAbAF4L1z/d+yv/G3Q\n6mzj3u8eot1lIzEygd8e/XOe3f4y2+p3UBCXz88W3OTf9r29H/Phvs8AuHDy2f7X2bt9XB4XhQ07\nGReXO+QRWA8He1tLKG7fg8epsih9YZ9iT5urgzeK32VdzSY0TWN53hJ+OP77ARcCu9vO3zY/6+++\njI2I4dY5Pw7oLrS7Hfxx7V+p7woEAO6cfyPj48cFHO+T/Sv9tR7JpiRumH1VwIB2To+Ll4r+y/7W\nMhZkzOXE3BP8F+CeXKqbfS37URTdoHfGOD3OEb3lWaeH5KTYIX+2VNtqKWrcRU5MFpMSx/d5XtVU\nPi1dxaau7NGcrq7dYF3e7S4b1q7ujoK4fHJis/q9hRy8dxd9vP9zQOGYzAUBwXZvLtWNW3X5s4Sq\npmK1FvHH+37PsmXLueqq64Jup2kaxc17WFW+mi1128mNzeKYzIUcl7UoaMHvtvodvLZrBR3uDqYl\nWdjVvKdP12FvExMKuO/7vxjxyEOCjiB6Bx3BLm4+TfZm7l7tHbH03Imnc2Le4rCey+HkQIOOUD1X\n+DIbardgNkZz14JbeWvPB/4PEYCfzLqyTxGaS3Xz8y9/i0t1sTxvCWeMP4XPSr/kvZL/+dOzC9Pn\nceX0i/oc7187/sua6vXoFT3TkidT2V5NQ1c9RlxELPce9+t+63R6Xhh6Mih6bp5zLZO6sh/9WVn+\nDa/t6ns7XdcHQ79t/lnplxQ37+XE3OP7vfV2U+02nin8V8Cya2Zc1ucW4fU1m3l++78BOD5rEWtr\nNuH0ODkh+5iAIrVWZxu/Wf3HgIHzFBQeXHzPmBibY6jv8063HYfH0e8AcS2ONt4oXkFMRAwn5y8N\nul5tRx0vFb1Ola2apbnH84NedT3gvQCtr9lMk6OZxdnHjIk27m2kP1sOldbWFs477wxycnL44x//\nwquvvsy77wa/jVZRFH70o6u46NLLBqzR8PFdyxVFwelx8eimp4LW7E1OmEC6OY3vFyxhcna+BB0h\nOihBx7b6Hfxj6wv+38fHj+PO+TcGXfeprf9kS713tMl7jrnLX4k+Fo30B8OOBiuPb3k26HOzU2f0\nOwLp45uf9ddR9JYQGc8tc64jI0iX1PaGnTyx5bk+yw2Knmtn/iholb1Pq7ONX39zb0AtQXxEHBdP\nOWfA7XzXL7qPAAAgAElEQVQ0TeOZwpfY3KNPPiEynv+36FYKMrMOuM3L2ir4quJbIvQRzEyeFjTr\nomoqj29+lp097kYB+PmCmxkXlxew7MUdrwak9DOi0/jN0aFNYnW4OlIugIeTI7XNW1qaaW/vPyMR\nFxcfMNZHKDpcHXxXvZHqjlp0KGSa0zk6c6F/zKeuNh/xoENqOoLoHYjZB6jp8AUcscaYMR1wHApT\nkyaTF5sTUIuhU3QcnTGfiyzn9Jt1+F7eYooad/Wp4F+cfQznTDy93zqXyYkT/SN/AugVPd8fdyJH\npc8jNXrgv21cRCzXzriMt3a/T11nQ1fh4fcGHebcR1EU78Bhu1awt2UfBfH5LM9bTIJpaOMUDCY3\nNnvA8UvA27bnTz6T//vuL/62GxeX1yfgAJiTOiMg6PDVDwghhi4+PoH4+IPT9RhtjA4ofD9cSNAR\nRO+LVbBKe/CmT32OzlxwUM/pSKQoCpdNPZ8/r/8bLtXFuZN+2O/txT1NSZrEVdMv4bntLwPe/vRf\nHXV70BE3ezLqDNw4+2pW7PmI2IhYLrKcFVIqe3bqDGanzhjy+r3pFB0XWs4a9vbhkGFO48TcE/is\n7EtSo5K5ctrFQdebkTKVSQnjKW7ei9kQzfHZR4/wmQohRiMJOoLo3eHU2U/QsaG2u8r9qIzwjacv\numXHZPLw0ntD3m5++uyg42YMJj8ul1vmBi/uOlKcM+l0TspfSrQhqt9RKnWKjlvmXEdhw04mJhQE\nLVwUQojeJOgIonf3isPjxKN6+nwA96wODlYjIMRoNdisu+C9hXuweWeEEKInmco0iN7dK+AdUKg3\n38iAuUEGYBFCCCFEILlSDlGwAcL8o+FFS5ZDCCGEGIx0rwShBRlzv3ddh0t1U9s1NfBQZkUVQghx\nZPjww/d47rmneO21FUGfv+8+7+jjv/rVb4e8z1WrvuCNN15l9+5inE4HBQUTOPvs8zjttB+G5ZxH\nigQdQQQbuaR3pqO0tdw/JkNuTP8j0gkhhDgS9T8Exm23hTamzQsvPMOLLz7HVVddx89+9v8wGo2s\nXbuGRx99iNbWFi666LIDPdkRI0FHEMEGTOud6fDNKKugUNA134UQQggxmOho85DX3bNnN88//zS/\n+929LFu23L/8zDPPISoqikceeZALLrgEnW50VEtI0BFEsELSjh6Zjp2Nxf6prDU0TIbIETs3IYQ4\nEnS6O6m21Y3Y8TLMqf45Uoaqrq6WRx99iA0b1qHTKSxffgo33fTTrmc1nnzycd5887+YzTFcd90N\nnHrq6UBo3Ssffvge48aNDwg4fE488STmzVswagIOkKAjqGCZDlvXKJUAG2u3+B9PTZo8IuckhBBH\nik53J79Z/ad+x0g6GKIMUfzh2F8OOfBwu93ccstPyMvL5/HHn6apqZEHHrgXRVGYPNlCdXUVe/fu\n5sknX6CoaDt//vN9ZGZmMWdOaGM6bd++jVmz5gR9zmAwkJKSGtL+DjUJOgYRbYiiw91JXUe9f1lx\n017/46U5xx2K0xJCCHEIrVnzDQ0N9Tz77IuYzTEUFIzn9tt/wV133c4tt9xBZGQkd9/9e2JjYxk3\nroBNmzbw9ttvhBx0tLQ0ExcXF7DswgvPorGxe3biv/zl0X4Dk8ONBB1BqD26V9KjUylpLaW2K+io\n62igttP7+BLLucxImXpIzlEIIcYqX9bhcO5e2b9/H7m5eZjN3QPpzZw5C4/Hg6qqZGVlB0zWZrFM\n4b33gs8oO5DY2Dja29sClj3yyD/weLyzPF988Tmo6uiZJE+CjmB6dK+k+YKOrkCj5+yl05ItI35q\nQghxJIgyRFEQ33eywcNFRETfWj6Px3vxV1UPul4jWKuqhsEQfLLJgUybNoMtWzYFLMvI6B6mYbTN\nFD96qk9GUM9CUt/AX82OFuo7G/jvrrcByDSnk2g6OLMDCiGEOLzl5eVTVlZKW1t3FqKwcAsGgwFF\n0VFRUY7D0T2SdVHRdvLzx4V8nNNPP5OSkj18/fWXfZ6rq6sd1rkfShJ0BNEz6Egzdxfp/Pbb+/2P\n56bKVN5CCHGkWrhwEVlZ2fzhD/8fe/fuZuPG9Tz88IOcdNIpxMbG4nQ6uPfe31FSspe3336DlSs/\n48ILLwn5OBMmTOT662/innt+zT//+Sx79+6hoqKcN998jWuv/RHp6RlkZGQdhFd4cEj3SjA9slUZ\n0cErg7+Xt2SETkYIIcThRqfTcf/9D/HQQw9w/fVXER0dzcknn8aPf3wjn376MZMmWUhNTeXHP76S\nhIREfvWr3zJp0vC65C+66DImTbLw6qsv8/rrr9LZ2UFOTh7nnnsh5513YUjjfhxqEnQE0bOQNMmU\niEFnwK26/cvuOeaXMjaHEEIc4TIyMnnggb/2WX7qqaf7x+S45ZY7+jzv8bgxmUIbE2T+/IXMn79w\neCd6GJHulaC6gw6doiOpR+2GUWcgJSrpUJyUEEKIUW7nziKKi3eNuvE1wkUyHUH0rAZWUMiOyfLf\nMnv5tIsO1WkJIYQYxVpbW7j11p+Qk5PDaaf9kEcf/Qvvvhv8NlpFUfjRj67iRz+6cmRP8iCToCOI\nnjcgKYrCDwtOptPVycSEAikgFUIIMSxxcfH873+r/L9fccU1nHvuhQOuP9ZI0BFE7/ue081p3DL3\nukN0NkIIIcai+PgE4uOPrKEXpKYjiJ63zCoDTE8shBBCiKGToCOIgKBDkaBDCCGECAcJOoLp6l6R\nLIcQQggRPhJ0BOHLc0iWQwghhAgfCTqC0BhdE+gIIYQQo8FBu3vFYrF8DLxstVpfHGCdccDTwDHA\nPuB2q9X6ycE6p6Hy3b2ik+4VIYQQImzCnumwWCyKxWJ5DFg+hNXfBiqB+cBLwFsWiyVnuMfeVr+D\nD0o+welxDXcXQI9Mh3SvCCGEEGET1kyHxWLJwhs8FADNg6x7IjAeONpqtdqBP1kslu8BVwO/D/XY\nHtXDs4Uv41JdOD0uzpp4WugvwMcXc0imQwghhAibcGc65gK78WYuWgdZdxGwsSvg8Pkab1dLSPZV\nt3Lnk1/gUr0Zjk21W0PdRQDfhG8ScgghhBDhE9ZMh9VqfR94H8BiGXQK30y8XSs91QAhda84XB5+\n/8J6lCgbpq5lLc7B4p3BSPeKEEIIEW4hBR0Wi8UEZPfzdJXVau0IYXfRgKPXMgcQ0pzx7Z3e7IZi\n6K7jcKluDAZvEuedr/bS2Obg8lMs6HV9EzuqqrF+Zy3lde0smZNNcrzJH2voUPz7EaDX6wJ+ioNP\n2nzkSZuPPGnzkXeo2jrUTMci4AsIek/p2cCKEPZlB3rPER8JhBK4EBPTld8wOAOWJyaa2VfVyhur\n9gIwMS+RM06YELDO2h3V/OHZ7/y/v/1VCW898ENMUUYAFJ1CYqI5lNM5IsTFRR3qUzjiSJuPPGnz\nkSdtPvaFFHRYrdZVhK8OpAKY1mtZBlAVyk5sNm+yRDEGBh21DS18sa7U//vTbxdywowM/+81jR0B\nAYfP2b94l/MvVr371BSammyhnM6YptfriIuLorW1E49HPdSnc0SQNh950uYjT9p85PnafKQdyllm\n1wB3WSyWSKvV6utmOR74KpS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K7P5s6lnD4jNQsW97p4uaxg7GZ8UNqSh4NDMYdCQmmmlq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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fce5de4c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the objectives\n",
    "plt.plot(fs_D, label=\"obj_D\")\n",
    "plt.plot(fs_G, label=\"obj_G\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QUiAHVLdxIwCOYcOJO2Zip503fvoJVH/7Nb76eqqXf0v8cVM77dwiHVHTXMuW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\ngXXAt1RV9RgaWJxRFMUB/AG4GX3a8v9QVfU3xkYV/xRFGQ78FliEXm4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fce5e00278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ploter_g_d()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
